Building Machine Learning models with Apache Spark and Amazon SageMaker | AWS Floor28

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Julien Simon
Principal Technical Evangelist, AI and Machine Learning
@julsimon
Using Apache Spark
with Amazon SageMaker
October 2018

Agenda
• Apache Spark on AWS
• Amazon SageMaker
• Combining Spark and SageMaker
• Demos with the SageMaker SDK for Spark
• Getting started
Services covered: Amazon EMR, Amazon SageMaker

Apache Spark on AWS

Apache Spark
• Open-source, distributed processing system.
• In-memory caching and optimized execution for fast performance
(typically 100x faster than Hadoop).
• Batch processing, streaming analytics, machine learning, graph
databases and ad hoc queries.
• API for Java, Scala, Python, R, and SQL.
https://spark.apache.org/

Apache Spark – DataFrame
• Distributed collection of data organized into named columns.
• Conceptually equivalent to a table in a relational database.
• Wide array of sources: structured files, databases.
• Wide array of formats: text, CSV, JSON, Avro, ORC, Parquet.
{"name": "Jeff"}
{"name": "Boaz", "age":72}
{"name": "Julien", "age":12}
df = spark.read.json("people.json")
df.show()
+----+-------+
| age| name |
+----+-------+
|null| Jeff |
| 72 | Boaz |
| 12 | Julien|
+----+-------+

MLlib – Machine learning library
• ML algorithms: classification, regression, clustering, collaborative filtering.
• Featurization: feature extraction, transformation, dimensionality reduction.
• Tools for constructing, evaluating and tuning ML pipelines
• Transformer – a transform function that maps a DataFrame into a new one
• Adding a column, changing the rows of a specific column, etc.
• Predicting the label based on the feature vector.
• Estimator – an algorithm that trains on data
• Consists of a fit() function that maps a DataFrame into a Model.
https://spark.apache.org/docs/latest/ml-guide.html

Spark ML on Amazon EMR: spam detector
Adapted from https://github.com/databricks/learning-spark/blob/master/src/main/scala/com/oreilly/learningsparkexamples/scala/MLlib.scala
ETL
Train
Predict

Apache Spark on Amazon EMR
• Spark is natively supported in Amazon EMR.
• Amazon S3 connectivity using the EMR File System (EMRFS).
• Amazon Kinesis, Redshift and DynamoDB as data sources.
• Integration with the AWS Glue Data Catalog.
• Auto Scaling to add or remove instances from your cluster.
• Integration with the Amazon EC2 Spot market.
https://aws.amazon.com/emr/

Amazon SageMaker

Amazon SageMaker
Collect and prepare
training data
Choose and optimize
your ML algorithm
Set up and manage
environments for
training
Train and tune model
(trial and error)
Deploy model
in production
Scale and manage
the production
environment
Easily build, train, and deploy Machine Learning models
FREE
TIER

Amazon SageMaker
Pre-built
notebooks for
common
problems
K-Means Clustering
Principal Component Analysis
Neural Topic Modelling
Factorization Machines
Linear Learner
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq,
And more!
ALGORITHMS
Apache MXNet, Chainer
TensorFlow, PyTorch
Caffe2, CNTK,
Torch
FRAMEWORKS
S e t u p a n d m a n a g e
e n v i r o n m e n t s f o r
t r a i n i n g
T r a i n a n d t u n e
m o d e l ( t r i a l a n d
e r r o r )
D e p l o y m o d e l
i n p r o d u c t i o n
S c a l e a n d m a n a g e t h e
p r o d u c t i o n e n v i r o n m e n t
Built-in, high-
performance
algorithms
Build

Amazon SageMaker
Pre-built
notebooks for
common
problems
Built-in, high-
performance
algorithms
One-click
training
Hyperparameter
optimization
Build Train
Deploy model
in production
Scale and manage
the production
environment

Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high-
performance
algorithms
One-click
training
Hyperparameter
optimization
Build Train Deploy

Amazon ECR
Model Training (on EC2)
Model Hosting (on EC2)
Trainingdata
Modelartifacts
Training code Helper code
Helper codeInference code
GroundTruth
Client application
Inference code
Training code
Inference requestInference response
Inference Endpoint
Amazon SageMaker

Combining Spark and SageMaker

Decouple ETL and Machine Learning
• Different workloads require different instance types.
• Say, M4 for ETL, P3 for training and C5 for prediction?
• If you need GPUs for training, running your EMR cluster on GPU
instances wouldn’t be cost-efficient.
• Scale them independently.
• Avoid oversizing your Spark cluster.
• Avoid time-consuming resizing operations on EMR.
• Run ETL once, train many models in parallel.
• SageMaker terminates training instances automatically.

Run any ML algorithm in any language
• Spark MLlib is great, but you may need something else.
• SageMaker built-in algorithms (ML, DL, NLP).
• Deep Learning libraries, like TensorFlow or Apache MXNet.
• Your own custom code in any language.

Deploy ML models in production
• Perform ML predictions without using Spark.
• Save the overhead of the Spark framework.
• Save loading your data in a DataFrame.
• Improve latency for small-batch predictions.
• It can be difficult to achieve low-latency predictions with Spark ML models.
• You can get real-time predictions with models hosted in SageMaker.
• You can use very powerful instances for prediction endpoints.

Sample use cases for Spark+SageMaker
• Data preparation and feature engineering before training.
• Data transformation before batch prediction (model reuse).
• Data enrichment with predictions.
• Predict missing values instead of using median.
• Add new predicted features.
• Train on extremely large datasets with built-in algos.

SageMaker SDK for Spark
• Python and Scala SDK, for Apache Spark 2.1.1 and 2.2.
• Pre-installed on EMR 5.11 and later.
• Train, import, deploy and predict with SageMaker models directly from
your Spark application.
• Standalone,
• Integration in Spark MLlib pipelines.
• DataFrames in, DataFrames out:
automatic conversion to and from protobuf (crowd goes wild!)
https://github.com/aws/sagemaker-spark

SageMaker SDK for Spark – built-in algorithms
• High-level API for:
• Linear Learner
• Factorization Machines
• K-Means
• PCA
• LDA
• XGBoost
• The SageMakerEstimator object lets you use other built-in containers
as well any containerized code stored in Amazon ECR (just like the
regular SageMaker SDK).
https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
Infinitely scalable algorithms:
no limit to the amount of data that they can process

Demos
https://gitlab.com/juliensimon/dlnotebooks
1 – Classifying MNIST in Python with XGBoost (SageMaker)
2 – Clustering MNIST in Scala with K-Means (SageMaker)
3 – Clustering MNIST in Scala with a Pipeline: PCA (MLlib) + K-Means (SageMaker)

Getting started
https://ml.aws
https://aws.amazon.com/sagemaker
https://aws.amazon.com/emr
https://github.com/aws/sagemaker-python-sdk
https://github.com/aws/sagemaker-spark
https://medium.com/@julsimon
https://gitlab.com/juliensimon/dlnotebooks

Thank you!
Julien Simon
Principal Technical Evangelist, AI and Machine Learning
@julsimon

Building Machine Learning models with Apache Spark and Amazon SageMaker | AWS Floor28

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